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hey all, my chapter 5 work is in the repo - I tried it with a loop (as in the pseudocode) and with dplyr::mutate() in a dataframe, and even with purrr::pmap and base::apply(), and in this case, seems like the loop is the fastest way to do it. I'd be curious to see if anyone came up with a different way to take advantage of vector operations to do it faster.
I also included some code from econometrics, calculating ordinary least squares estimators in matrix form, which is a much more efficient way (as in thousands of times faster) to find the parameters that minimize the sum of squares... but a lot less conceptually intuitive than the loop process. If folks are interested I'd be happy to show how it works on Wednesday.
The text was updated successfully, but these errors were encountered:
Also, I think I was losing steam by using this pretty round-about pseudo code which helps to understand the concepts but isn't what we'd use to do this in real life. Maybe we could talk about this Weds if there's extra time.
hey all, my chapter 5 work is in the repo - I tried it with a loop (as in the pseudocode) and with
dplyr::mutate()
in a dataframe, and even withpurrr::pmap
andbase::apply()
, and in this case, seems like the loop is the fastest way to do it. I'd be curious to see if anyone came up with a different way to take advantage of vector operations to do it faster.I also included some code from econometrics, calculating ordinary least squares estimators in matrix form, which is a much more efficient way (as in thousands of times faster) to find the parameters that minimize the sum of squares... but a lot less conceptually intuitive than the loop process. If folks are interested I'd be happy to show how it works on Wednesday.
The text was updated successfully, but these errors were encountered: